class_docstr: DreamerV3 specific Learner class.
  
  Only implements the `additional_update_for_module()` method to define the logic
  for updating the critic EMA-copy after each training step.
code: def compile_results(
      self,
      *,
      batch: MultiAgentBatch,
      fwd_out: Dict[str, Any],
      loss_per_module: Dict[str, TensorType],
      metrics_per_module: DefaultDict[ModuleID, Dict[str, Any]],
  ) -> Dict[str, Any]:
      results = super().compile_results(
          batch=batch,
          fwd_out=fwd_out,
          loss_per_module=loss_per_module,
          metrics_per_module=metrics_per_module,
      )
  
      # Add the predicted obs distributions for possible (video) summarization.
      if self.config.report_images_and_videos:
          for module_id, res in results.items():
              if module_id in fwd_out:
                  res[
                      "WORLD_MODEL_fwd_out_obs_distribution_means_BxT"
                  ] = fwd_out[module_id]["obs_distribution_means_BxT"]
      return results
  
file_path: ray/python/ray/rllib/algorithms/dreamerv3/dreamerv3_learner.py
imports: from typing import Any, DefaultDict, Dict
  from ray.rllib.algorithms.dreamerv3.dreamerv3 import DreamerV3Config
  from ray.rllib.core.learner.learner import Learner
  from ray.rllib.policy.sample_batch import MultiAgentBatch
  from ray.rllib.utils.annotations import override
  from ray.rllib.utils.typing import ModuleID, TensorType
label_desc: Use image node_id 1 for calling the DreamerV3Learner obj's underlying member method code with example usage: obj.compile_results() and returns: results